flair.models.EntityMentionLinker#

class flair.models.EntityMentionLinker(candidate_generator, preprocessor, entity_label_types, label_type, dictionary, batch_size=1024)View on GitHub#

Bases: Model[Sentence]

Entity linking model for the biomedical domain.

__init__(candidate_generator, preprocessor, entity_label_types, label_type, dictionary, batch_size=1024)View on GitHub#

Initializes an entity mention linker.

Parameters:

  • candidate_generator (CandidateSearchIndex) – Strategy to find matching entities for a given mention

  • preprocessor (EntityPreprocessor) – Pre-processing strategy to transform / clean entity mentions

  • entity_label_types (Union[str, Sequence[str], dict[str, set[str]]]) – A label type or sequence of label types of the required entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only ‘disease’ and ‘chemical’ labels from a NER-tagger: {‘ner’: {‘disease’, ‘chemical’}}. To use all labels from ‘ner’, pass ‘ner’

  • label_type (str) – The label under which the predictions of the linker should be stored

  • dictionary (EntityLinkingDictionary) – The dictionary listing all entities

  • batch_size (int) – Batch size to encode mentions/dictionary names

Methods

__init__(candidate_generator, preprocessor, ...)

Initializes an entity mention linker.

add_module(name, module)

Add a child module to the current module.

apply(fn)

Apply fn recursively to every submodule (as returned by .children()) as well as self.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

buffers([recurse])

Return an iterator over module buffers.

build(model_name_or_path[, label_type, ...])

Builds a model for biomedical named entity normalization.

children()

Return an iterator over immediate children modules.

compile(*args, **kwargs)

Compile this Module's forward using torch.compile().

cpu()

Move all model parameters and buffers to the CPU.

cuda([device])

Move all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Set the module in evaluation mode.

evaluate(data_points, gold_label_type[, ...])

Evaluates the model.

extra_repr()

Set the extra representation of the module.

extract_entities_mentions(sentence, ...)

Extract tagged mentions from sentences.

float()

Casts all floating point parameters and buffers to float datatype.

forward(*input)

Define the computation performed at every call.

forward_loss(data_points)

Performs a forward pass and returns a loss tensor for backpropagation.

get_buffer(target)

Return the buffer given by target if it exists, otherwise throw an error.

get_entity_label_types(entity_label_types)

Find out what NER labels to extract from sentence.

get_extra_state()

Return any extra state to include in the module's state_dict.

get_parameter(target)

Return the parameter given by target if it exists, otherwise throw an error.

get_submodule(target)

Return the submodule given by target if it exists, otherwise throw an error.

half()

Casts all floating point parameters and buffers to half datatype.

ipu([device])

Move all model parameters and buffers to the IPU.

load(model_path)

Loads a Flair model from the given file or state dictionary.

load_state_dict(state_dict[, strict, assign])

Copy parameters and buffers from state_dict into this module and its descendants.

modules()

Return an iterator over all modules in the network.

mtia([device])

Move all model parameters and buffers to the MTIA.

named_buffers([prefix, recurse, ...])

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix, remove_duplicate])

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse, ...])

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Return an iterator over module parameters.

predict(sentences[, top_k, pred_label_type, ...])

Predicts the best matching top-k entity / concept identifiers of all named entities annotated with tag input_entity_annotation_layer.

print_model_card()

This method produces a log message that includes all recorded parameters the model was trained with.

register_backward_hook(hook)

Register a backward hook on the module.

register_buffer(name, tensor[, persistent])

Add a buffer to the module.

register_forward_hook(hook, *[, prepend, ...])

Register a forward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Register a forward pre-hook on the module.

register_full_backward_hook(hook[, prepend])

Register a backward hook on the module.

register_full_backward_pre_hook(hook[, prepend])

Register a backward pre-hook on the module.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module's load_state_dict() is called.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module's load_state_dict() is called.

register_module(name, module)

Alias for add_module().

register_parameter(name, param)

Add a parameter to the module.

register_state_dict_post_hook(hook)

Register a post-hook for the state_dict() method.

register_state_dict_pre_hook(hook)

Register a pre-hook for the state_dict() method.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

save(model_file[, checkpoint])

Saves the current model to the provided file.

set_extra_state(state)

Set extra state contained in the loaded state_dict.

set_submodule(target, module)

Set the submodule given by target if it exists, otherwise throw an error.

share_memory()

See torch.Tensor.share_memory_().

state_dict(*args[, destination, prefix, ...])

Return a dictionary containing references to the whole state of the module.

to(*args, **kwargs)

Move and/or cast the parameters and buffers.

to_empty(*, device[, recurse])

Move the parameters and buffers to the specified device without copying storage.

train([mode])

Set the module in training mode.

type(dst_type)

Casts all parameters and buffers to dst_type.

xpu([device])

Move all model parameters and buffers to the XPU.

zero_grad([set_to_none])

Reset gradients of all model parameters.

Attributes

T_destination

call_super_init

dictionary

dump_patches

label_type

Each model predicts labels of a certain type.

model_card

training

get_entity_label_types(entity_label_types)View on GitHub#

Find out what NER labels to extract from sentence.

Parameters:

entity_label_types (Union[str, Sequence[str], dict[str, set[str]]]) – A label type or sequence of label types of the required entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only ‘disease’ and ‘chemical’ labels from a NER-tagger: {‘ner’: {‘disease’, ‘chemical’}}. To use all labels from ‘ner’, pass ‘ner’

Return type:

dict[str, set[str]]

property label_type#

Each model predicts labels of a certain type.

property dictionary: EntityLinkingDictionary#
extract_entities_mentions(sentence, entity_label_types)View on GitHub#

Extract tagged mentions from sentences.

Return type:

list[Label]

predict(sentences, top_k=1, pred_label_type=None, entity_label_types=None, batch_size=None)View on GitHub#

Predicts the best matching top-k entity / concept identifiers of all named entities annotated with tag input_entity_annotation_layer.

Parameters:

  • sentences (Union[list[Sentence], Sentence]) – One or more sentences to run the prediction on

  • top_k (int) – Number of best-matching entity / concept identifiers

  • entity_label_types (Union[str, Sequence[str], dict[str, set[str]], None]) – A label type or sequence of label types of the required entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only ‘disease’ and ‘chemical’ labels from a NER-tagger: {‘ner’: {‘disease’, ‘chemical’}}. To use all labels from ‘ner’, pass ‘ner’

  • pred_label_type (Optional[str]) – The label under which the predictions of the linker should be stored

  • batch_size (Optional[int]) – Batch size to encode mentions/dictionary names

Return type:

None

classmethod build(model_name_or_path, label_type='link', dictionary_name_or_path=None, hybrid_search=True, batch_size=128, similarity_metric=SimilarityMetric.INNER_PRODUCT, preprocessor=None, sparse_weight=0.5, entity_type=None, dictionary=None, dataset_name=None)View on GitHub#

Builds a model for biomedical named entity normalization.

Parameters:

  • model_name_or_path (str) – the name to an transformer embedding model on the huggingface hub or “exact-string-match”

  • label_type (str) – the label-type the predictions should be assigned to

  • dictionary_name_or_path (Union[str, Path, None]) – the name or path to a dictionary. If the model name is a common biomedical model, the dictionary name is asigned by default. Otherwise you can pass any of “gene”, “species”, “disease”, “chemical” to get the respective biomedical dictionary.

  • hybrid_search (bool) – if True add a character-ngram-tfidf embedding on top of the transformer embedding model.

  • batch_size (int) – the batch_size used when indexing the dictionary.

  • similarity_metric (SimilarityMetric) – the metric used to compare similarity between two embeddings.

  • preprocessor (Optional[EntityPreprocessor]) – The preprocessor used to preprocess. If None is passed, it used an AD3P processor.

  • sparse_weight (float) – if hybrid_search is added, the sparse weight will weight the importance of the character-ngram-tfidf embedding. For the common models, this will be overwritten with a specific value.

  • entity_type (Optional[str]) – the entity type of the mentions

  • dictionary (Optional[EntityLinkingDictionary]) – the dictionary provided in memory. If None, the dictionary is loaded from dictionary_name_or_path.

  • dataset_name (Optional[str]) – the name to assign the dictionary for reference.

Return type:

EntityMentionLinker

forward_loss(data_points)View on GitHub#

Performs a forward pass and returns a loss tensor for backpropagation.

Implement this to enable training.

Return type:

tuple[Tensor, int]

classmethod load(model_path)View on GitHub#

Loads a Flair model from the given file or state dictionary.

Parameters:

model_path (Union[str, Path, dict[str, Any]]) – Either the path to the model (as string or Path variable) or the already loaded state dict.

Return type:

EntityMentionLinker

Returns:

The loaded Flair model.

evaluate(data_points, gold_label_type, out_path=None, embedding_storage_mode='none', mini_batch_size=32, main_evaluation_metric=('accuracy', 'f1-score'), exclude_labels=None, gold_label_dictionary=None, return_loss=True, k=1, **kwargs)View on GitHub#

Evaluates the model. Returns a Result object containing evaluation results and a loss value.

Implement this to enable evaluation.

Parameters:

  • data_points (Union[list[Sentence], Dataset]) – The labeled data_points to evaluate.

  • gold_label_type (str) – The label type indicating the gold labels

  • out_path (Union[str, Path, None]) – Optional output path to store predictions.

  • embedding_storage_mode (str) – One of ‘none’, ‘cpu’ or ‘gpu’. ‘none’ means all embeddings are deleted and freshly recomputed, ‘cpu’ means all embeddings are stored on CPU, or ‘gpu’ means all embeddings are stored on GPU

  • mini_batch_size (int) – The batch_size to use for predictions.

  • main_evaluation_metric (tuple[str, str]) – Specify which metric to highlight as main_score.

  • exclude_labels (Optional[list[str]]) – Specify classes that won’t be considered in evaluation.

  • gold_label_dictionary (Optional[Dictionary]) – Specify which classes should be considered, all other classes will be taken as <unk>.

  • return_loss (bool) – Weather to additionally compute the loss on the data-points.

  • **kwargs – Arguments that will be ignored.

Return type:

Result

Returns:

The evaluation results.